Indian Journal of Science and Technology, Vol 8(35), DOI: 10.17485/ijst/2015/v8i35/79955, December 2015
ISSN (Print) : 0974-6846
ISSN (Online) : 0974-5645
An Efficient Neighbor Discovery and Aloha based
Collision Detection and Correction in VANET
K. Chandramohan1* and P. Kamalakkannan2
1Department
of Computer Science and Engineering, Gnanamani College of Engineering, Namakkal - 637018,
Tamil Nadu, India; [email protected]
2Department of Computer Science, A. A. Government Arts College, Namakkal - 637002,
Tamil Nadu, India; [email protected]
Abstract
Objectives: This paper proposes a novel method to addresses the problem of detecting and correcting collision attack
in VANET and enhances the security of the nodes. Methods: The proposed Collision detection-based neighbor discovery
model uses local monitoring to observe the behavior of neighborhood node. The collision detection is then shared with
the immediate neighbors using warning message and propagated to a neighboring region with aiming at improving the
broadcasting rate. In addition, proposed NDA-CDS uses Aloha-based Collision Correction which enables a randomized
model that provides redundant information, allowing each individual node to process data packet using time slots to
reduce time delay in correcting the colliding nodes which in turn maximizes the security of the nodes. The results are
simulated in NS2 with each data packet size is differing from 7-49 byte. Findings: The NDA-CDC framework based on their
immediate neighbors enhances the security of the nodes (i.e., vehicles) and reduces the delay time for detecting collision
attack. The experimental results demonstrated that the proposed NDA-CDS model out performed than the existing state
of the art works in terms broadcast rate, security and delay time for detecting and correcting collision. Improvement:
The simulation result shows that proposed NDA-CDS method has advantages over the other existing system in terms of
reducing the delay time for detecting collision attack and improving the security.
Keywords: Aloha-Based Collision Correction, Collision Attack, Message Authentication Protocol, Mix-Zone, Neighbor
Discovery, Vehicular Ad Hoc Network
1. Introduction
Attack-Resilient Mix-zones over Road Networks (ARMRN)1 used mix-zone construction methods to avoid
attacks by maintaining higher level of anonymity. Expedite
Message Authentication Protocol (EMAP)2 for Vehicular
Ad Hoc Networks applied Hash Message Authentication
Code to ensure security for packets being transmitted.
However, both the methods were not based on the trajectory patterns which increased the time for detection.
The proposed framework addressed delay time by
applying Aloha-based collision correction. Effective
*Author for correspondence
data aggregation3 was performed in the presence of colliding attacks using Iterative Filtering. Another method
designed in4 was called as non-parametric approach for
effective traffic classification5 that reduce delay time using
Nearest Neighbor classifier. However, with the absence
of beacon messages, though delay time was reduced,
acknowledgement of receipt of data packets was not made
in between the nodes. Inter-Vehicular communication5
was introduced to provide dynamic beaconing ensuring
latency.
One of the emerging applications in Vehicular Ad hoc
Network (VANET) is Intelligent Transportation Systems
An Efficient Neighbor Discovery and Aloha based Collision Detection and Correction in VANET
(ITS). Direction based Hazard Routing Protocol6 was
designed using Road Side Units (RSUs) in VANET to
improve the number of vehicles received at RSU and minimize the dissemination delay. However, measures were
not introduced to address the same issues with different
traffic and security remained unaddressed. To solve the
issues related to security in VANET, authentication mechanisms7 were introduced that minimize the rate of false
positive condition by improving the trust level practically.
However, effective collision detection technique was not
introduced. The proposed framework addresses this issue
by introducing a Poisson approximation to improve the
collision detection rate.
Collision probability estimation scheme8 were used
Cooperative Awareness Message for measuring inter vehicle communication to reduce collision attack. However,
possible trajectories remained unaddressed. The framework NDA-CDC uses neighborhood node to determine
the trajectories. Based on the trajectory of neighborhood
node, Nash Bargaining from game theory9 was introduced to improve the bandwidth utilization in VANET. A
survey of misbehaving nodes in VANET10 was presented
to improve the safety on roads and driving conditions.
Road traffic simulation for Inter Vehicle Communication (IVC)11 was designed with the objective of improving
the average speed and coverage of network in the presence of attack. Another IVC method12 was designed with
the objective of improving the service coverage. In addition, Bilinear Diffie Hellman method13 was developed to
increase the message integrity in VANET. However, differentiation between normal and malicious nodes was not
made in an efficient manner.
Based on the time of collision, Early Warning Intelligent Broadcasting algorithm14 was introduced to improve
the broadcasting rate. However, the road conditions were
not considered during broadcasting. An efficient cluster
authentication scheme15 was introduced to ensure secure
data packet transmission in the presence of replay attack.
However, security remained unaddressed. The framework NDA-CDC applied Neighbor Discovery algorithm
for secure data packet transmission in VANET.
The information regarding malicious node by the surrounding vehicles is enabled by in-vehicle sensors and
VANETs. Information hiding techniques16 contains two
hiding techniques namely, subliminal channels and steganogrpahy that improve the robustness against communication errors. A survey regarding adaptive beaconing17 was
presented to improve the transmission of beacons using
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Adaptive transmission and Adaptive contention window.
However, collision detection remained unaddressed. Furthermore, eMAC protocol and Energy-VeMAC18 was
introduced to prevent the collision and to improve the
rate of throughput in message transfer between the vehicles. Collision detection and prevention for Unmanned
Ground Vehicle in military battlefield using WSN based
VANET19 was developed to improve driving safety. InterVehicular Collision avoidance system20 was designed to
perform safety communication with each other which
can alert the drivers before accidents.
A novel MCBA architecture21 was introduced for protecting applications against memory corruption attacks
by incorporating efficient encoding of memory contents
that reduced execution and protected the storage overhead. The centralized FEC algorithm22 was more adaptive
in adjusting and controlling the data redundancy across
the network for static and dynamic channel fluctuations.
Proactive parking based data replication method23 was
developed to improve the data accessibility in VANET.
TPM-based Architecture24 was designed in VANET which
used a TPM component embedded in vehicles to improve
security and anonymity of VANET communication.
In this paper we present NDA-CDC, a collision detection and correction framework in VANET to protect data
packets and traffic. Compared to the existing methods,
the NDA-CDC framework has a number of unique features. First, the NDA-CDC framework collision detection
is developed based on the neighbor region and location
information in assessing the neighbor nodes. This in
turn improves the broadcast rate. Second, we introduce
a Neighbor Discovery Algorithm based on local monitoring to improve the security of the data packets being
transmitted by vehicles and therefore avoiding collision
attack in VANET. Third, we develop a Aloha-based Collision Correction model to reduce the delay time in identifying the colliding nodes at an early stage. We formally
analyze and experimentally validate the robustness of our
NDA-CDC framework against collision timing attacks.
2. Design of Neighbor Discovery
based Collision Detection
and Aloha-based Collision
Correction
In this section we introduce our Neighbor Discovery
based Collision Detection and Aloha-based Collision
Indian Journal of Science and Technology
K. Chandramohan and P. Kamalakkannan
Correction framework for VANET in detail. We also
present Neighbor Discovery (ND) algorithm in details.
The NDA-CDC framework is implemented under
the neighborhood region framework and employs neighboring region to observe the behavior for efficient collision detection and correction in VANET. The Block
diagram of Neighbor Discovery based Collision Detection and Aloha-based Collision Correction is shown in
the Figure 1.
As shown in Figure 1, the NDA-CDC framework
to detect and correct the colliding attack in VANET is
designed. The proposed Neighbor Discovery based Collision Detection and Aloha-based Collision Correction
(NDA-CDC) framework uses sensor data collected by
vehicles in VANET with the objective of reducing the
delay time for collision detection and correction. The
framework with the objective of improving the security
of data packets being transmitted shares with immediate
neighbors, and propagates them to a neighboring region.
The validity of the sensor data is checked by individual
vehicles in VANET with the objective of checking the
validity of the data packets. NDA-CDC framework is
split into three parts namely 1. Collision Detection-based
Neighbor Discovery, 2. Neighbor Discovery algorithm
based on location and 3. Aloha-based Collision Correction. The design of three parts is discussed elaborately in
the following sections.
Collision Detection-based Neighbor discovery
Warning message
Routing table
Network - VANET
Neighbor Discovery algorithm based on
location
Aloha-based Collision Correction
Figure 1. Block diagram of Neighbor Discovery based
Collision Detection and Aloha-based collision correction.
2.1 Collision Detection-based Neighbor
Discovery
One of the popular methods for detecting collision attacks
in VANET is performed by observing the neighborhood
node (i.e., vehicle) behavior or local monitoring of vehicles. Local monitoring by a vehicle efficiently monitors the
data traffic or message transmission performed in and out
Vol 8 (35) | December 2015 | www.indjst.org
of its neighbors. This is a join detection model where the
process of monitoring is designed in such a way that the
data packets or vehicles are verified by seeing to that they
are being faithfully forwarded without any modification.
Let us consider a design of vehicular ad hoc network
that consists of a set of nodes (i.e., vehicles) ‘Vi= V1,V2,…
Vn,’ , a set of Road Side Units (RSUs) ‘RSUi= RSU1,RSU2,..
RSUn,’ and Certificate Authority ‘CA’. Suppose a node ‘Vi’
sends a warning message ‘WMi’ at time ‘t’ and is formulated as given below.
åni=1 Vi  (WMi , t )
(1)
Once a node ‘Vj’ receives warning message ‘WMi’ from
‘Vi’, then the NDA-CDC depends upon the sensor data,
collected by nodes that are shared with immediate neighbors, and passed to the neighboring region. The warning message consists of three tuples and is formulated as
given below
åni=1 WMi  (Vi , Loci , t )
(2)
From Equation (2) the warning message ‘WMi’
includes the vehicle from where the warning message
originated ‘Vi’ the location of the vehicle ‘Loci’ and the
time, ‘t’ at which the warning message originated. After
an interval of time ‘t+i’, when ‘Vi’ receives a beacon message from vehicle ‘Vj’, checks for the validity of location.
Upon successful validity of location data packet transmission is performed between the vehicles. Figure 2
shows the neighbor discovery based on changed location (Figure 2 (a)) and without changes in location (Figure 2 (b)).
Let us consider Figure 2 (a) with two vehicles ‘V1,
V2’. The vehicle ‘V1’ sends a warning message ‘WM1’ to
vehicle ‘V2’ at time ‘t’. After an interval of time ‘t+i’, when
vehicle ‘V1’ receives a beacon message from vehicle ‘V2’,
the vehicle ‘V1’ checks whether it is the neighboring node
using location information.
As shown in Figure2 (a), the location of the vehicles
has been changed whereas in Figure 2 (b) the location
remains unchanged. In the NDA-CDC, with the objective of improving the broadcasting rate, the information
regarding collision is shared with immediate neighbors and propagated to the neighboring region. The
NDA-CDC then includes a routing table that consists of
details including sensed node (i.e., vehicle), location and
detected node which is maintained by ‘CA’. Table 1 shows
the description of routing table.
Indian Journal of Science and Technology
3
An Efficient Neighbor Discovery and Aloha based Collision Detection and Correction in VANET
(a)
algorithm. The ND algorithm works with the objective
of improving the security of the nodes which sends data
packet to the destined or detected node. The ND algorithm works with the principle that the nodes share data
packet with only the immediate neighbor nodes with the
assumption that the neighboring nodes are valid nodes
than the distance nodes which are considered as collided
nodes.
(b
)
Figure 2. Shows the neighbor discovery based on changed
location. (a) Changed location. (b) No changes in location.
As shown in Table 1, the nodes observe the sensed
nodes that have been already detected successfully with
immediate neighbors. With the aid of the above routing
table, local monitoring by a vehicle is made in an efficient
manner, aiming at improving the broadcasting rate. The
NDA-CDC using neighbor discovery with the help of
routing table checks to see if the location is contradictory.
If both the location of the sensed node and detected node
are contradictory, the detected nodes location in addition
to the node is sent to the RSU by ‘CA’. As a result, only
non-contradictory detected nodes are further considered
in the network for data packet transmission. This in turn
increases the broadcasting rate.
Table 1. Structure of routing table
Sensed Node
Location
Detected Node
V1
(10,15)
V6
V2
(10,35)
V7
V3
(10,45)
V8
V4
(20,55)
V9
V5
(40,65)
V10
2.1.1 Neighbor discovery algorithm
With the objective of improving the security in the NDACDC, a neighbor discovery algorithm is designed using
local information. With the assumptions that data packet
communication over long distances is limited in bandwidth, the NDA-CDC uses the neighbor propagation
model. Figure 3 shows the Neighbor Discovery (ND)
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Initialize Vehicles ‘
Units ‘
’, Warning Message ‘
’
’, Time ‘ ’ Road Side
Output: Collision detected node
Step 1: Begin
Step 2: For each vehicle
Step 3:
Sends a warning message ‘
Step 4:
Vehicle
Step 5:
If location ( ) = location ( ) then
Step 6:
Perform data packet transmission
Else
Step 9:
Detected node is not neighbor node
Step 10:
Step 11:
checks for the warning message using (2) based on location
Detected node is neighbor node
Step 7:
Step 8:
’ at time ‘ ’ using () to vehicle
Detected node is colliding node
End if
Step 12: End for
Figure 3. Neighbor discovery algorithm.
Figure 3 illustrates the algorithmic description of
neighbor discover using local monitoring. Whenever a
vehicle wants to send a data packet, the immediate neighbor nodes are detected and the location of the detected
nodes is matched with the sender node’s location. Efficient broadcasting takes place only the detected nodes
location is same to that of the sender node’s location. If
both the location does not match, the detected node is
not the node that the sender node has intended to send
and is considered as the colliding node. This effective differentiation of normal nodes and colliding nodes helps
in improving the security against colluding adversaries.
Therefore detection and correction of malicious data is
ensured.
2.2 Aloha-based Collision Correction
In the previous section, Collision Detection-based Neighbor Discovery using Local Monitoring assumed that the
location information send in the warning message is
correct. However, a colliding node with the intention of
performing collision attack also sends wrong location
Indian Journal of Science and Technology
K. Chandramohan and P. Kamalakkannan
information, along with the false warning message. In this
section we see, how to detect incorrect location information using Aloha-based Collision Correction model.
The NDA-CDC considers the Aloha-based Collision
Correction model when all the ‘n’ vehicles in VANET
are arranged in a group. In Aloha-based Collision Correction model, we consider a slot where time interval ‘T’
is divided into slots. Therefore, each message (i.e., data
packet) transmission starts when the slot is initiated and
lasts the entire duration of the slot.
Figure 4 shows the Aloha-based random model where
the vehicle sends a data packet independently in a time
slot ‘T’ with probability ‘Pneighdis’. The aloha-based random
model includes two self-loops, one with labels ‘Pneighdis
and 1’ and another with labels ‘Pneighdis and 0’.
3. Experimental Settings
In this section we evaluate the performance of NDA-CDC
via simulation. NDA-CDC has been compared to AttackResilient Mix-zones over Road Networks (ARM-RN)1
and Expedite Message Authentication Protocol (EMAP)2
for Vehicular Ad Hoc Networks. The simulation parameter used for effective data transmission is given below
(Table 2). The vehicles in NDA-CDC are positioned in
uniform topology.
Table 2. Simulation parameters
Figure 4. Aloha-based random model.
The Aloha-based Collision Correction in NDA-CDC
framework is a randomized model that operates as follows. During each slot, a vehicle transmits a SAFETY
message broadcast announcing its ID, with probability of
proximity ‘Pneighdis’. It listens with the probability of proximity ‘1-Pneighdis’ , where the SAFETY message broadcast
is performed in a given slot only if exactly one vehicle
sends data traffic in that slot.
Let ‘Vi(t)’ be a random variable that represents the total
successful data packet transmissions in VANET by vehicle
‘i’ in the first ‘T’ slots. By applying Poisson approximation
in the NDA-CDC the success data packet transmissions
in ‘T’ slots is as given below:
P (Vi (t )) =
e-a * en
(3)
n!
From Equation (3), the probability of successful data
packet transmission at time slot ‘t’ is equal to the ratio
of product of the event ‘e’ that discovered the vehicle
‘i’ and ‘a’ representing data packet ‘p’ at time slot ‘’.
With this Aloha-based Collision Correction model,
the probability of collision is detected at an early stage.
Therefore, the delay time in identifying the collision
is reduced in the NDA-CDC using randomized Aloha
model.
Vol 8 (35) | December 2015 | www.indjst.org
Parameters
Values
Network area
1500 m * 1500 m
Vehicle density
10,20,30,40,50,60,70
Number of data packets i.e.,
number of data block
5,10,15,20,25,30,35
Size of data block (i.e., packet)
7 – 49 bytes
Range of communication
50 M
Speed of node
0 – 10 m/s
Simulation time
500 ms
Number of runs
7
The network consists of 70 vehicles, placed in a random manner in the Vehicle Ad hoc Network that generates traffic for every 10 m/s. The neighboring vehicle (i.e.,
node) collects the data packets of range 5-35 and forwards
the data with each data packet size differing from 7-49
bytes. The simulation time varies from 100 simulation
milliseconds to 500 simulation milliseconds and the following metrics like broadcast rate, security and delay time
for detecting and correcting colliding nodes in VANET
are measured.
The broadcast rate measures the amount of data packet
received successfully. The broadcast rate is the ratio of
data packets received to the data packets sent in VANET.
It is measured in terms of percentage (%)
BR =
D Pr
* 100
D Ps
(4)
From Equation (4), the broadcast rate ‘BR’, measures
the ratio of data packet received ‘DPr’ and data packets sent
‘DPs’. Security with respect to data packets is measured on
the basis of data packets received at the neighboring node
in VANET. Therefore, security is the difference between
the total packets sent to the packets not received in the
neighboring node.
Indian Journal of Science and Technology
5
An Efficient Neighbor Discovery and Aloha based Collision Detection and Correction in VANET
S(D P) = D Ps - D Pnr
(5)
From Equation (5), ‘DPs’ refers to the data packets
sent and ‘DPnr’ refers to the data packets not received
in the neighboring node in VANET. It is measured in
terms of packets per second (pps). Delay time is the time
taken to identify the collision in VANET with respect
to varied node density. It is measured in terms of
milliseconds (ms). Delay time is the product of time
taken to identify the colliding node with respect to
node density.
DT = Time (CN ) * ND
(6)
Broadcast rate with respect to number of data
Table 3.
packets
Number of Data
Packets
Broadcast Rate (%)
NDA-CDC
ARM-RN
EMAP
5
78.52
58.34
40.33
10
81.29
69.29
64.21
15
83.16
71.16
66.08
20
80.22
68.22
63.14
25
84.31
72.31
67.24
30
81.35
69.35
64.29
35
85.89
73.89
68.81
From Equation (6), ‘DT’ refers to the delay time with
respect to colliding node ‘CN’ and node density ‘ND’.
4. Discussion
The result analysis of Neighbor Discovery based Collision
Detection and Aloha-based Collision Correction (NDACDC) framework is compared with the existing AttackResilient Mix-zones over Road Networks (ARM-RN)1
and Expedite Message Authentication Protocol (EMAP)2
for Vehicular Ad Hoc Networks.
Table 3 represents the broadcast rate obtained using
NS2 simulator and comparison is made with two other
methods, namely ARM-RN1 and EMAP2. Figure 5 shows
the result of broadcasting rate efficiency that measures the
amount of data packet received successfully in VANET
versus the varying number of data packets in the range of
7-49 bytes. To better perceive the efficacy of the proposed
NDA-CDC framework, substantial experimental results
are illustrated in Figure 4 and compared against the existing ARM-RN1 and EMAP2 respectively. The broadcast
rate efficiency for different observations made by several
nodes is performed at different time interval is shown
above. Higher, the number of data packets being sent (i.e.,
the observation), more successful the framework is. The
results reported here confirm that with the increase in the
number of data packets, the broadcast rate efficiency also
increases. The process is repeated for 35 different data
packets.
As illustrated in Figure 5, the proposed NDA-CDC
framework performs relatively well when compared
to two other methods ARM-RN1 and EMAP2. The
broadcasting rate efficiency using NDA-CDC framework is improved with the application of Collision
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Vol 8 (35) | December 2015 | www.indjst.org
Figure 5. Measure of broadcast rate.
Detection-based Neighbor Discovery where message
transmission is performed in and out of its neighbors.
As a result, by applying local monitoring for each vehicle
based on the neighboring node, results in the improvement of broadcasting rate efficiency using NDA-CDC
framework by 19.83% and 34.25% compared to ARMRN1 and EMAP2 respectively.
In Table 4 we compare the security with different number
of data packets in VANET setting. The experiments were
conducted with 35 observations (i.e., data packets) and the
security obtained is measured in terms of packets per second (pps). In order to improve the security while observing
the neighboring nodes that does not receive the data packets, the data packets sent and data packets not received is
considered. In the experimental setup, the number of data
packets ranges from 5 to 35 is illustrated in Figure 6. The
security using the NDA-CDC framework provides comparable values than the state-of-the-art methods.
The targeting results of number of data packets sent
to measure the security using NDA-CDC framework is
compared with two state-of-the-art methods ARM-RN1
and EMAP2 in Figure 6. Our framework NDA-CDC
Indian Journal of Science and Technology
K. Chandramohan and P. Kamalakkannan
Security with respect to number of data
Table 4.
packets
Number of data
packets
ARM-RN
Delay time with respect to node density
Node Density
Security (pps)
NDA-CDC
Table 5.
Delay Time (ms)
EMAP
NDA-CDC
ARM-RN
EMAP
10
220
260
295
5
4
3
3
20
235
265
300
10
7
6
4
30
249
279
312
15
11
10
9
40
240
270
278
20
17
16
13
50
255
285
293
25
20
19
17
60
242
272
280
30
22
20
19
70
249
279
287
35
27
23
21
Figure 6. Measure of security.
Figure 7. Measure of delay time.
differs from the ARM-RN1 and EMAP2 in that we have
incorporated Neighbor Discovery algorithm where
immediate neighbors are identified based on the location
using warning message and checks for the validity of location. Based on the location validity, the results are generated and stored in routing table which is sent to RSU and
maintained by CA. As a result, the security is improved by
15% and 28.24% compared to the ARM-RN1 and EMAP2
respectively.
Table 5 shows the delay time for collision detection
and correction with respect to node density of size 70
vehicles in VANET. Figure 7 given shows the delay time
for collision detection and correction for NDA-CDC
framework, ARM-RN1 and EMAP2 versus seven different
nodes (i.e., vehicles). The delay time returned over NDACDC framework increases gradually though not linear
for differing node size because of the dynamic changes in
the network and topology in VANET.
From Figure 7, it is illustrative that the delay time is
reduced using the proposed framework NDA-CDC. For
example with node density 30, the delay time was 249
ms using NDA-CDC whereas ARM-RN1 recorded 279
ms and 312 ms using EMAP2. Though the delay time for
measuring collision detection and correction using all
the three methods increase gradually, but using the proposed NDA-CDC, it is comparatively less. This is because
with the application of Aloha-based Collision Correction.
With the help of Aloha-based Collision Correction, a randomized model applies Poisson approximation that helps
in identifying the collision at an early stage in an extensive
manner. This in turn helps in reducing the delay time by
14.97% compared to ARM-RN1. In addition, by applying
randomized Aloha model helps in improving the probability of successful data packet transmission using Poisson distribution and therefore reducing the delay time for
collision detection and correction by 24.49% compared
to EMAP2.
Vol 8 (35) | December 2015 | www.indjst.org
Indian Journal of Science and Technology
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An Efficient Neighbor Discovery and Aloha based Collision Detection and Correction in VANET
5. Conclusion
Neighbor Discovery based Collision Detection and
Aloha-based Collision Correction (NDA-CDC) framework in VANET to reduce the delay time for collision
detection and correction and improve security for vehicles on dynamic observations is introduced. We then
showed how this framework can be extended to incorporate Collision Detection-based Neighbor Discovery
model to improve the broadcast rate based on neighbor
node by propagating with neighboring regions. The Collision Detection-based Neighbor Discovery model also
provided efficient mapping of vehicles using warning
message based on the location and hence improved the
data packet transmission efficiency in VANET. This location information were arrived by determining the location using the routing table maintained by CA that in
turn increases the broadcasting rate efficiency. Next, the
introduced Neighbor Discovery algorithm reduces the
data packet drop rate using local monitoring to improve
the rate of security of nodes carrying data traffic in the
network. This local monitoring and location information
improves the security of vehicles. The results show that
NDA-CDC framework offers better performance with an
improvement of broadcast rate efficiency on data packets
being traversed in VANET by 27.04% compared to the
state of the art works.
6. References
1. Palanisamy B, Liu L. Attack-resilient mix-zones over road
networks: Architecture and algorithms. IEEE Transactions
on Mobile Computing. 2015 Mar; 14(3):495–508.
2. Wasef A, Shen X. EMAP: Expedite Message Authentication
Protocol for vehicular ad hoc networks. IEEE Transactions
on Mobile Computing. 2013 Jan; 12(1):78–89.
3. Rezvani M, Ignjatovic A, Bertino E, Jha S. Secure data aggregation technique for wirelesssensor networks in the presence of collusion attacks. IEEE Transactions on Dependable
and Secure Computing. 2015 Apr; 12(1):98–110.
4. Zhang J, Xiang Y, Wang Y, Zhou W, Xiang Y, Guan Y. Network traffic classification using correlation information.
IEEE Transactions on Parallel and Distributed Systems.
2013; 24(1):104–17.
5.Sommer C, Joerer S, Segata M, Tonguz OK, Cigno RL,
Dressler F. How shadowing hurts vehicular communications and how dynamic beaconing can help. IEEE Proceedings INFOCOM; Turin. 2013. p. 110–4.
8
Vol 8 (35) | December 2015 | www.indjst.org
6.Berlin MA, Anand S. Direction based Hazard Routing
Protocol (DHRP) for disseminating road hazard information using road side infrastructures in VANETs. Berlin and
Springer Plus. 2014; 3:1–12.
7.Pattnaik O, Pattanayak BK. Security in vehicular ad hoc
network based on intrusion detection system. American
Journal of Applied Sciences. 2014; 11(4):37–46.
8. Joerer S, Segata M, Bloessl B, Cigno RL, Sommer C, Dressler
F. A vehicular networking perspective on estimatingvehicle
collision probability at intersections. IEEE Transactions on
Vehicular Technology. 2014; 63(4):1802–12.
9.Schwartz RS, Ohazulikey AE, Sommerz C, Scholten H,
Dresslerz F, Havinga P. On the applicability of fair and
adaptive data dissemination in traffic information systems.
Ad Hoc Networks. 2014; 13(Part B):428–43.
10. Khan U, Agrawal S, Silakari S. A detailed survey on misbehavior node detection techniques in vehicular ad hoc
networks. Information Systems Design and Intellignet
Applications, Springer. 2015; 339:11–9.
11. Sommer C, German R, Dressler F. Bidirectionally coupled
network and road traffic simulation for improved IVC
analysis. IEEE Transactions on Mobile Computing. 2011;
10(1):3–15.
12.Malandrino F, Casetti C, Chiasserini C-F, Sommer C,
Dressler F. The role of parked cars in content downloading for vehicular networks. IEEE Transactions on Vehicular
Technology. 2014; 63(9):4606–17.
13. Huang MW, Wu HT, Horng G-J, Hsieh W-S. Using BDH for
the message authentication in VANET. Mathematical Problems in Engineering. 2014; 2014:13.
14.Bae IH. An intelligent broadcasting algorithm for early
warning message dissemination in VANETs. Computational Science and its Application-ICCSA. Switzerland:
Springer International Publishing; 2014. p. 668–81.
15. Jeon YB, Lee KH, Park DS, Jeong C-S. An efficient cluster authentication scheme based on vanet environment in
m2m application. International Journal of Distributed Sensor Networks. 2013; 2013:9.
16.de Fuentes JM, Blasco J, Gonzalez-Tablas AI, GonzalezManzano L. Applying information hiding in vanets to
covertly report misbehaving vehicles. International Journal
of Distributed Sensor Networks. 2014; 2014:15.
17. Bouk SH, Kim G, Ahmed SH, Kim D. Hybrid adaptive beaconing in vehicular ad hoc networks: a survey. International
Journal of Distributed Sensor Networks. 2015; 2015:1–16.
18.Santhosh DD, Krishnaveni A. Effective collision detection using E-VeMAC in VANET. International Journal of
Advanced Research in Computer Science and Software
Engineering. 2014; 4(3):128–31.
19.Thakur N, Kumar P. A cooperative strategy for collision detection and prevention for unmanned ground
Indian Journal of Science and Technology
K. Chandramohan and P. Kamalakkannan
vehicles in military applications using WSN based VANET.
International Journal of Computer Applications. 2014;
102(14):20–6.
20. Devdhara G, Gohil D, Akhade P, Vala M. Inter-vehicular
collision detection and avoidance using ad-hoc network.
International Journal for Research in Emerging Science and
Technology. 2015; 2(2):1–7.
21.Robert E, Hemalatha. Epidemic dynamics of malicious
code detection architecture in critical environment. Indian
Journal of Science and Technology. 2014; 7(6):770–5.
22.Lakshmi, Banu W. Centralised enhanced forward error
correction mechanism on the contention window to
Vol 8 (35) | December 2015 | www.indjst.org
improve the transmission quality in vehicular ad hoc networks. Indian Journal of Science and Technology. 2015;
8(20):52680.
23. Ghavifekr MH, Khosrowshahi AC. Parking based data replication in VANET. Indian Journal of Science and Technology. 2015; 8(27):70054.
24. Suresh JS, Jongkun L. A TPM-based architecture to secure
VANET. Indian Journal of Science and Technology. 2015;
8(15):IPL034.
Indian Journal of Science and Technology
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